Shadow elimination and vehicles classification approaches in traffic video surveillance context

Abstract

Video surveillance on highway is a hot topic and a great challenge in Intelligent Transportation Systems. In such applications requiring objects extraction, cast shadows induce shape distortions and object fusions interfering performance of high level algorithms. Shadow elimination allows to improve the performances of video object extraction, classification and tracking. In other hand, it is very important to recognize the type of a detected object in order to track reliably and estimate traffic parameters correctly. This paper presents two approaches to enhance automatic traffic surveillance systems. The first deals with the elimination of shadows and the second concerns the classification of vehicles, based on robust vision and image processing. For moving shadow elimination, a contrast model is proposed to describe and remove dynamic shadows based on the idea that a shadow transformation is a change in contrast. For vehicles classification, Hu moments are calculated in a manner to reduce the perspective effects and used to describe vehicles in knowledge base. Experimental results on the various challenging video sequences show that the proposed approach outperforms classification methods of related works (with a classification accuracy of 96.96%), and that the shadow elimination approach performs better than compared works (with detection rate of 95–99% and discrimination rate of 85.7–89%).